基于Petri网仿真的柔性生产调度——蚁群-遗传递阶进化优化方法

利用受控赋时Petri网对柔性生产线调度中的离散事件建模,此Petri网模型由过程流子网、资源子网和调度控制子网通过同步变迁连接而成.在由Petri网仿真运行获得调度性能评价的基础上,采用两级递阶进化优化方法求解柔性生产过程的优化调度问题.首先由蚁群优化方法优化加工路径,然后根据蚁群在信息素指引下所构造的加工路径,采用遗传算法优化在同一机器上加工的作业排序.应用蚁群优化原理提出了加工路径优化问题的信息素表达方式,解构造策略和信息素更新策略.一组测试问题的求解结果说明了算法的有效性和鲁棒性.

Flexible jobshop scheduling based on Petri-net model:ACO-GA hierarchical evolutionary optimization approach

A controlled timed Petri-net was used to model discrete events in flexible production lines scheduling. This model was synthesized by process-flow subnet, resource subnet and scheduling subnet by means of synchronous common transition interconnection. A hierarchical evolutionary optimization approach for flexible jobshop scheduling problems was proposed based on the simulation evaluation via the Petri-net model. The processing route of jobs was optimized by an ant colony optimization (ACO) approach, and, on the basis of the optimal processing route, a genetic algorithm was proposed to optimize the sequence of operations processed on each machine. On the principle of ACO, the representation of pheromone, the strategy of solution construction and pheromone updating for routing problem of jobs were proposed. A benchmark problem solving result indicates that the proposed algorithm is effective.